Working with numerous clients across all industry verticals, it seems that many of them feel that using machine learning and other AI solutions is somehow out of reach. That’s just not the case!

Your Machine Learning Roadmap

Getting started with machine learning is like many other strategic projects and technology investments. You’ll want to follow this basic roadmap.

1. Select a problem of significant scale you want to solve.

Referring back to my previous example on sentiment analysis, there is no way a human can ingest thousands of comments or reviews (written and video) regarding a new online game release. Computers, however, can process this information with the power afforded by machine learning. Machine learning converts that data into real insights that can be used to address a shortcoming of the game or to alter the marketing.

2. Determine the desired outcome.

In this example, we want to identify any key flaws or shortcomings of the game. Ultimately, the goal is to release the best product possible that will generate positive reviews and in turn will drive proliferation of the game online.

3. Assess your organization’s data.

Determine whether additional data is needed to fill gaps to enable learnings to be captured. In other words, is the “online plumbing” in place to capture the game play comments and reviews? This is the raw data that will be consumed by the machine learning algorithms.

4. Create a small task force to evaluate platforms.

This task for should evaluate different machine learning platforms used to deliver personalized, relevant communication at scale and solve other problems of scale. Most machine learning solutions fall in one of the following categories:

Speech/Text/Language

Pattern Recognition and Prediction

Vision

For our game, sentiment analysis would be performed utilizing a machine learning solution specifically designed to analyze and derive insight from text and language. For example, IBM’s Watson Alchemy Language API and Google's Cloud Natural Language API have robust text and language analytic capabilities.

5. Develop and execute a small pilot.

Make sure there is a clear test plan and learning agenda for the pilot. You need to ensure a “roadmap” is in place to fully evaluate and draw conclusions regarding key aspects of gameplay, the gameplay environment, etc.

6. Assess the results.

Determine if you achieved the desired outcome. Did certain aspects of gameplay not meet desired expectations? If so, specifically identify what needs to be modified.

7. Iterate and improve.

Make the necessary modifications to the game and re-release along with promotional communication touting game improvements.

8. Roll out at scale.

Once desired outcome has been met, roll-out the game at scale.

Machine learning and other solutions within the AI family are here to stay. To compete in a digital world, companies must focus their resources on creating truly custom and personalized experiences at every touch point in the customer journey. They must also be able to accurately evaluate whether these experiences are delivering the desired outcomes and continuously improve upon them.

To create and evaluate these experiences, patterns, predictions, and insights must be derived from terabytes and terabytes of data. Machine learning and other AI solutions can make it happen—and this technology is within your reach.

About the Author

As Chief Data and Analytics Officer for Harte Hanks, Korey Thurber manages ideation, development & delivery for customer analytics and data solutions. With more than 20 years of experience managing large international teams of data scientists, analysts & data solutions providers, Korey has helped many brands to successfully adapt & integrate analytics to develop, execute and optimize marketing strategies across diverse online and offline media. His extensive knowledge & experience has made him a valuable asset for many clients across multiple industry verticals including Financial Services, Wealth Management, Retail/CPG and Non-Profit.